Digitized Classification And Application Of Soybean Variety Maturity Groups In China

Soybean varieties generally adapt to a narrow geographical zone due to the ir sensitivity to light and temperature. The interregional introduction of the varieties often leads to either early or late maturity, which reduces the production or brings the production risk. To identify the optimum adaptation zone of soybean varieties, soybean maturity group identification was carried out at 36 sites of research platform of the National Soybean Industrial Technology System of China from 2011 to 2015 by using the international soybean maturity group(MG) digitized classification system. The regional distribution of soybean production and quality were analyzed accordingly. Results are as follows:(1) According to the days to physiological maturity(DPM) of North American soybean maturity group reference varieties at each experimental site, we clarified the identifying range of maturity group under natural light conditions in different ecoregions. Meanwhile, eight sites, namely Heihe, Zhalantun, Suihua, Beijing, Xuzhou, Changchun, Nanchong and Wuhan were selected as the representative identifying sites of soybean maturity group for different ecoregions. From 2014 to 2015, a set of new North American soybean maturity group reference varieties(MG accurate to 0.1) were sowed at the eight identifying sites in spring. Fine MGs accurate to 0.1 of the representative reference varieties were identified by using linear regression model. The same method was applied to the 840 major soybean cultivars of China from 19 sites in the North Spring Planting Soybean Region(NSR), 9 sites in the Huang-Huai-Hai Summer Soybean Planting Region(HHHR), and 8 sites in South Multi-cropping Soybean Region(SMCR).(2) Visual analysis was performed using the soybean MGs data from 784 sampling sites to determine the soybean ecoregion by the method of Kriging interpolation of Arc GIS software, and finally got the soybean ecological regionalization based on the MG classification. The results showed that soybean varieties of MGIII and MGIV distributed most widely in China. The distribution zone for soybean MGs in China agrees with that in U.S. in the north region of 42°N, but differs in the south region of 42°N due to the different cropping systems.(3) Soybean cultivars of appropriate MGs and 1 and 2 groups earlier were sown and assessed at 17 sites of major production areas in China to identify the MG range of the cultivars which can safely mature under different late sowing conditions. A set of soybean MGs collocation scheme for reseeding after natural disaster was achieved according to the results,which can provide reference for reseeding after disaster and establishing seed reserve for reseeding.(4) Soybean fell into different MGs greatly varied in production. The yield of MGII soybean varieties ranked first, followed by that of MG0 and MGI soybean varieties, and the yield of MGV-IX soybean varieties was the lowest. Coefficient of variation for soybean yield of MGII and MGV-IX were much higher than those of other MGs, indicating the large variance in yield within groups. Plant height of MG000-II soybean varieties was significantly higher than that of MGIII and other later MGs. Soybean varieties of MGV-IX have the maximum number of pods per plant, and MG0 soybean had the minimum. Average 100-seed weight of MGIII and MGIV was the largest among all the MGs. The stepwise regression showed that plant density was the key yield limiting factor for the soybean of MG0 and MGV-IX, 100-seed weight for the soybean of MGI, and seed number per plant for the soybean of MGII-IV.(5) Chemical quality analysis was performed to 763 soybean samples of major soybean production regions collected from 2010 to 2013. The results showed that the protein content of MGII-IX soybean varieties and the oil content of MGII soybean varieties were highest, respectively. The correlation between climatic factors and soybean compositions was analyzed. It was showed that the contents of crude protein, water-soluble protein and the total of protein plus oil were positively correlated with the accumulated temperature above or equal to 15oC(AT15) and the mean daily temperature(MDT), but negatively correlated with hours of sunshine(HS) and the diurnal temperature range(DTR). The correlations of crude oil with climate factors were opposite to those of crude protein. Further analysis confirmed that crude oil content had quadratic regression relationship with MDT, and there was a positive correlation between oil content and MDT when the daily temperature was below 19.7oC. Path analysis indicated that DTR was the most direct climatic factor affecting soybean protein and oil contents and the total of protein plus oil. Appropriate agronomic measures were put forward to improve soybean quality in different soybean production regions according to the results.